Multi-sensor data classification in remote sensing using MRF regional growing algorithm

Author

Lee, Sanghoon ; Suh, Asook ; Jung, Myunghee

Author_Institution

Dept. of Ind. Eng., Kyungwon Univ., Kyunggi, South Korea

Volume

6

fYear

2001

fDate

2001

Firstpage

2884

Abstract

This paper studies a multi-stage method using hierarchical clustering for unsupervised image classification to classify the land-cover using remotely-sensed data from multiple sensors. The multi-stage method performs region-growing segmentation using a hierarchical clustering procedure which makes use of the spatial contextual information by characterizing geophysical connectedness of digital image structure with Markov random field

Keywords

Markov processes; airborne radar; image classification; image segmentation; pattern clustering; remote sensing by radar; sensor fusion; synthetic aperture radar; terrain mapping; unsupervised learning; MRF regional growing algorithm; Markov random field; digital image structure; geophysical connectedness; hierarchical clustering; land-cover; multi-sensor data classification; multi-stage method; region-growing segmentation; remote sensing; remotely sensed data; spatial contextual information; unsupervised image classification; Bayesian methods; Geophysical measurements; Image classification; Image segmentation; Image sensors; Layout; Pixel; Remote sensing; Sensor fusion; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-7031-7

Type

conf

DOI

10.1109/IGARSS.2001.978194

Filename

978194